Many technical systems like machines, search engines for databases and internet, e-commerce applications etc. require complex decisions and search or control strategies. A plurality of input data is to be evaluated in order to determine a specific probability of a situation.
The use of probability based systems for this aim is well known.
A standard probability logic makes directly use of the classical axioms of probability theory described by A. Kolmogorov: Grundbegriffe der Wahrscheinlichkeitsrechnung. Berlin: Springer-Verlag, 1933. The first two axioms state that probabilities are positive (non-negativity) and that a true proposition has the probability of one (normalization). The conjunction rules can be derived directly from the third axiom of (finite) additivity (sometimes δ-additivity). The probability of an even set, which is the union of an other disjoined subset of events is the sum of the probabilities of those subsets.
There are alternative calculi of probability of believe which have abandoned or extended the axioms of probability theory. However, even, for instance, Baconian probabilities, belief Functions, and the t-norms of fuzzy logic adhere to the conjunction rule stating that the probability of a first event set can never become larger than the probability of a second event set being part of the first event set, i.e. the second event set includes the intention (sense) and the extension (actual elements of a set) of the first event set.
WO 2007/084669 A2 discloses a system and method for electing subjective probabilities making use of Bayesian probabilistic networks among others. The system and method is used for dynamically interacting with a human expert by means of a graphical user interface to elicit subjective probabilities that can subsequently be utilized in a probabilistic network. A Bayesian network in designed to predict one or more outcomes based on a variety of causal relationships. A probability table is associated with each variable of the Bayesian network and information such as subjective probabilities. The Bayesian network is elicited.
U.S. Pat. No. 7,328,201 B2 discloses a system and method of using synthetic variables to generate relational Bayesian network models of internet user behaviors. Relational Bayesian modelling combines a relational data model with the probabilistic semantics needed to effectively model the stochastic elements of the observable behavior.
U.S. Pat. No. 5,704,017 discloses a system for collaborative filtering by use of a Bayesian network. The Bayesian network has learned using prior knowledge and a database containing empirical data obtained from many people containing attributes of users as well as their preferences in the field of decision making. The network accuracy is improved by re-learning at various intervals.
WO 2007/038713 A2 discloses a search engine determining results based on probabilistic scoring of relevance. An overall semantic relevance value for the occurrence of a term is determined by statistically combining an assignor relevance score determined for a plurality of assignors. The assignor relevance scores for the occurrence of the terms are determined by statistically combining an accuracy value and a consistency value.
The Bayesian logic is useful for automated filtering of data and for decision making. The object of the present invention is to provide an improved system and method for the inductive (data-based) determination of a specific kind of pattern probabilities of all 16 dyadic logical connectors suited for technically predicting human behavior, e.g. inclusion fallacies.